"# Restricted-boltzmann-machine" 2 little Projects using RBM model :

1. Mushroom Classification with Review-Based Model :

This project focuses on classifying mushrooms from the Agaricus and Lepiota family into their respective edibility categories. The dataset used contains descriptions of hypothetical samples of 23 different mushroom species. The classification is done using a Review-Based Model (RBM), which leverages natural language processing and machine learning techniques to make predictions based on user reviews and descriptions of mushrooms.

The dataset used for this project includes descriptions of mushrooms, their species, and their edibility status. It's essential to note that determining the edibility of a mushroom is a complex task, and there are no simple rules, as indicated in the dataset's description.

2. TripAdvisor Review Recommendation System:

   This project is focused on creating a recommendation system for TripAdvisor users who have reviewed and rated destinations in East Asia. The system takes into account each traveler's ratings, which are
   mapped to categories such as Excellent, Very Good, Average, Poor, and Terrible, and provides tailored recommendations for future trips.

   Dataset Information:

The dataset used in this project is sourced by crawling TripAdvisor.com. It includes reviews of destinations in 10 different categories across East Asia. Each traveler's rating is mapped to one of the following categories: Excellent (4), Very Good (3), Average (2), Poor (1), and Terrible (0). The average rating is used to represent the user's opinion in each category.

3. RBM Spam Email Classifier:

   This project is an implementation of a Restricted Boltzmann Machine (RBM) for classifying spam emails. RBMs are a type of artificial neural network commonly used for feature learning and data reduction tasks. In this project, we train an RBM on a dataset of emails to distinguish between spam and non-spam messages.

   Dataset Description:

   The instances in this dataset represent emails.

   Classification Task

The main classification task for this dataset is to determine whether a given email is spam or not. Spam emails encompass a diverse range of content, including advertisements, make-money-fast schemes, chain letters, and pornography.